Conditional cash transfer program and child mortality: A cross-sectional analysis nested within the 100 Million Brazilian Cohort.

BACKGROUND: Brazil has made great progress in reducing child mortality over the past decades, and a parcel of this achievement has been credited to the Bolsa Família program (BFP). We examined the association between being a BFP beneficiary and child mortality (1-4 years of age), also examining how this association differs by maternal race/skin color, gestational age at birth (term versus preterm), municipality income level, and index of quality of BFP management. METHODS AND FINDINGS: This is a cross-sectional analysis nested within the 100 Million Brazilian Cohort, a population-based cohort primarily built from Brazil's Unified Registry for Social Programs (Cadastro Único). We analyzed data from 6,309,366 children under 5 years of age whose families enrolled between 2006 and 2015. Through deterministic linkage with the BFP payroll datasets, and similarity linkage with the Brazilian Mortality Information System, 4,858,253 children were identified as beneficiaries (77%) and 1,451,113 (23%) were not. Our analysis consisted of a combination of kernel matching and weighted logistic regressions. After kernel matching, 5,308,989 (84.1%) children were included in the final weighted logistic analysis, with 4,107,920 (77.4%) of those being beneficiaries and 1,201,069 (22.6%) not, with a total of 14,897 linked deaths. Overall, BFP participation was associated with a reduction in child mortality (weighted odds ratio [OR] = 0.83; 95% CI: 0.79 to 0.88; p < 0.001). This association was stronger for preterm children (weighted OR = 0.78; 95% CI: 0.68 to 0.90; p < 0.001), children of Black mothers (weighted OR = 0.74; 95% CI: 0.57 to 0.97; p < 0.001), children living in municipalities in the lowest income quintile (first quintile of municipal income: weighted OR = 0.72; 95% CI: 0.62 to 0.82; p < 0.001), and municipalities with better index of BFP management (5th quintile of the Decentralized Management Index: weighted OR = 0.76; 95% CI: 0.66 to 0.88; p < 0.001). The main limitation of our methodology is that our propensity score approach does not account for possible unmeasured confounders. Furthermore, sensitivity analysis showed that loss of nameless death records before linkage may have resulted in overestimation of the associations between BFP participation and mortality, with loss of statistical significance in municipalities with greater losses of data and change in the direction of the association in municipalities with no losses. CONCLUSIONS: In this study, we observed a significant association between BFP participation and child mortality in children aged 1-4 years and found that this association was stronger for children living in municipalities in the lowest quintile of wealth, in municipalities with better index of program management, and also in preterm children and children of Black mothers. These findings reinforce the evidence that programs like BFP, already proven effective in poverty reduction, have a great potential to improve child health and survival. Subgroup analysis revealed heterogeneous results, useful for policy improvement and better targeting of BFP.

Evaluating the health effect of a Social Housing programme, Minha Casa Minha Vida, using the 100 million Brazilian Cohort: a natural experiment study protocol.

INTRODUCTION: Social housing programmes have been shown to influence health, but their effects on cardiovascular mortality and incidence of infectious diseases, such as leprosy and tuberculosis, are unknown. We will use individual administrative data to evaluate the effect of the Brazilian housing programme Minha Casa Minha Vida (MCMV) on cardiovascular disease (CVD) mortality and incidence of leprosy and tuberculosis. METHODS AND ANALYSIS: We will link the baseline of the 100 Million Brazilian Cohort (2001-2015), which includes information on socioeconomic and demographic variables, to the MCMV (2009-2015), CVD mortality (2007-2015), leprosy (2007-2015) and tuberculosis (2007-2015) registries. We will define our exposed population as individuals who signed the contract to receive a house from MCMV, and our non-exposed group will be comparable individuals within the cohort who have not signed a contract for a house at that time. We will estimate the effect of MCMV on health outcomes using different propensity score approaches to control for observed confounders. Follow-up time of individuals will begin at the date of exposure ascertainment and will end at the time a specific outcome occurs, date of death or end of follow-up (31 December 2015). In addition, we will conduct stratified analyses by the follow-up time, age group, race/ethnicity, gender and socioeconomic position. ETHICS AND DISSEMINATION: The study was approved by the ethic committees from Instituto Gonçalo Muniz-Oswaldo Cruz Foundation and University of Glasgow Medical, Veterinary and Life Sciences College. Data analysis will be carried out using an anonymised dataset, accessed by researchers in a secure computational environment according to the Centre for Integration of Data and Health Knowledge procedures. Study findings will be published in high quality peer-reviewed research journals and will also be disseminated to policy makers through stakeholder events and policy briefs.

Evaluating the impact of the Bolsa Familia conditional cash transfer program on premature cardiovascular and all-cause mortality using the 100 million Brazilian cohort: a natural experiment study protocol.

INTRODUCTION: Brazil's Bolsa Familia Program (BFP) is the world's largest conditional cash transfer scheme. We shall use a large cohort of applicants for different social programmes to evaluate the effect of BFP receipt on premature all-cause and cardiovascular mortality. METHODS AND ANALYSIS: We will identify BFP recipients and non-recipients among new applicants from 2004 to 2015 in the 100 Million Brazilian Cohort, a database of 114 million individuals containing sociodemographic and mortality information of applicants to any Brazilian social programme. For individuals applying from 2011, when we have better recorded income data, we shall compare premature (age 30-69) cardiovascular and all-cause mortality among BFP recipients and non-recipients using regression discontinuity design (RDD) with household monthly per capita income as the forcing variable. Effects will be estimated using survival models accounting for individuals follow-up. To test the sensitivity of our findings, we will estimate models with different bandwidths, include potential confounders as covariates in the survival models, and restrict our data to locations with the most reliable data. In addition, we will estimate the effect of BFP on studied outcomes using propensity score risk-set matching, separately for individuals that applied ≤2010 and >2011, allowing comparability with RDD. Analyses will be stratified by geographical region, gender, race/ethnicity and socioeconomic position. We will investigate differential impacts of BFP and the presence of effect modification for a combination of characteristics, including gender and race/ethnicity. ETHICS AND DISSEMINATION: The study was approved by the ethics committees of Oswaldo Cruz Foundation and the University of Glasgow College of Medicine and Veterinary Life Sciences. The deidentified dataset will be provided to researchers, and data analysis will be performed in a safe computational environment without internet access. Study findings will be published in high quality peer-reviewed research articles. The published results will be disseminated in the social media and to policy-makers.

EMJ series on statistics and methods: variables, populations and samples

This the first article in the EMJ Series on Statistics and Methods describes "variables" which represent different demographic and clinical characteristics of several individuals in a data set. Various attributes of quantitative and qualitative variables, including type, scale of measurement and values they may take are presented. Some descriptive statistics and graphic summaries are introduced. Pertinent examples drawn from journal articles are provided. The article also defines "population" as used in statistics and epidemiology and highlights population in size and scope in the context of the research question to be answered. It emphasizes the need for explicitly defining sampling based on the research question in a particular study. In addition, the articles introduces concepts and notations related to parameter and statistic. It highlights the need for using appropriate sampling method in selecting a representative sample of the study population and to be able to make valid inferences and generalizations. It is indicated in this articles that specific issues requiring further details will be addressed in the Series articles in subsequent Issues of EMJ

EMJ series on statistics and methods: normal distribution and the central limit theorem

This second article in the EMJ Series on Statistics and Methods dwells on the basics of sampling distribution of' variables', which are presented in detail in the preceding article in this Issue of the Ethiopian Medical Journal (EMJ). The present article highlights recommended routines that need to be undertaken in order to understand information collected in a particular study before embarking on doing complex statistical analyses. It underscores the importance of descriptive statistics as a means to getting insights into data quality and learn about the scale and distribution of different variables in a data set. The article emphasizes the need for assessing the sampling distributions of variables as a prerequisite to making decide on selection of appropriate statistical techniques for in a data set. It describes salient features of a normally distributed random variable and touches on some other probability distributions commonly used in epidemiological studies. The article also describes the central limit theorem highlighting salient points on its conceptual basis in understanding sampling distributions of sample means and the implications of using normal distribution to make inference about the population based on summary measures from a sufficiently large sample